The Graph's Scale: Actual Maximum Value or Potential Maximum Value?

Fine-tune your graphs by discussing this question with your colleagues: should our graph’s axes extend to the dataset’s actual maximum value or to the potential maximum value?Option A: Axis stretches just past the actual maximum value (30% in this example)
The biggest number is 26%, and the axis goes from 0% to 30%, just past the biggest number in the bunch. Your stakeholders will think the 26% looks enormous because it stretches all the way across the screen. Wow, look at our numbers! Great news! Of course, we’ll have to work on Category D, but we can certainly improve that number! Especially when the other categories are looking so good! Talk to your teammates: is this the message we’re going for?Option B: Axis stretches all the way to the potential maximum value (100% in this example)
What if the numbers have the potential to stretch all the way to 100%? (The percentage of attendees who said they’d recommend your conference to a colleague, the percentage of students who graduated on time, and so on.) If you’re trying to hit 100%, now the 26% isn’t looking so hot. Argh. We thought everything was going so well! We’ve got so much to improve upon. Where do we even start?! Ask your teammates: is this the right time to risk overwhelming the stakeholders with so much bad news?
My advice: Good facilitation skills are an ingredient of good data visualization. I often begin with the first graph to avoid scaring stakeholders away from action. As we get to know each other — and they get to know their numbers — I slowly introduce the second style.
When choosing minimum and maximum axis values, what other factors do you consider?